Reference architecture of common service platform for Industrial Big Data (I-BD) based on multi-party co-construction

  • Xianyu ZhangEmail author
  • Xinguo Ming
  • Dao Yin


In today’s information age with rapid development, all kinds of data have exploded. The Industrial Big data Application (I-BD) is generated by the continuous infiltration of big data in industry. Scholars have done a lot of research on the method, technology, and architecture of industrial big data from the perspective of data flow. However, there are relatively few studies on the reference model, reference architecture, and implementation path for industrial big data from the perspectives of detailed application scenarios, common service platform, and specific implementation. In this paper, firstly, the current situation of I-BD is analyzed. Secondly, a general reference model for I-BD is proposed, which consists of Industry (I) dimension, Application scenario (A) dimension, and common service platform (P) dimension. Further, the overall planning of application scenarios for I-BD based on industrial value chain for I-BD is studied. Again, a reference architecture of common service platform for I-BD based on multi-party co-construction is proposed. Finally, the implementation path of common service platform for I-BD is given. It can be used as a reference for industry and government to design, set, and carry out I-BD.


Big data Industrial big data Intelligent manufacturing Common service platform Application scenario Reference model 



The authors would like to thank Shanghai Key Laboratory of Advanced Manufacturing Environment, Shanghai Research Center for industrial Informatics (SRCI2), and SJTU Innovation Center of Producer Service Development (SICPSD) for the funding support to this research.


This work was supported by the National Natural Science Foundation of China [grant number 71632008] and Major Special Basic Research Projects for Aero engines and Gas turbines [grant number 2017-I-0007-0008, grant number 2017-I-0011-0012].


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© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Institute of Smart Manufacturing, School of Mechanical EngineeringShanghai Jiao Tong UniversityShanghai CityChina

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